Podcast Summary
The success of a collaborative agency lies in supporting end users:Â The Radiant Foundation emphasizes community-focused approaches and capacity building initiatives to make AI practical, productive, and accessible to everyone, contributing to the success of the end users they support.
The success of a collaborative agency lies in the success of the end users they support. This was emphasized during a discussion on the Practical AI podcast, where the hosts, Daniel Whitenak and Chris Benson, welcomed Joyce Nabinde and Ahmed Mohammed from the Radiant Foundation. The Radiant Foundation team shared their experiences in addressing AI data collection challenges, particularly in the context of machine learning for observation. They highlighted their community-focused approach and capacity building initiatives. The podcast also touched upon the importance of making AI practical, productive, and accessible to everyone. The collaboration between MIT Sloan Management Review, Boston Consulting Group, and the podcast aims to answer the question of why only 10% of companies succeed with artificial intelligence. Google Cloud's Will Granace was featured in a previous episode, discussing the human aspect of AI and the significance of understanding the meaning behind the correlations produced by AI algorithms. The podcast is available wherever podcasts are found, and you can join the community and follow them on Twitter for more conversations around AI, machine learning, and data science.
Earth Observation, Machine Learning, and the SDGs: Creating a Sustainable Future:Â Earth Observation provides satellite data for SDG targets, machine learning derives insights, governments and commercial sector provide data, accessing and using data from multiple sources is a challenge, effectively utilizing data is crucial for a sustainable future.
Earth Observation, machine learning, and the Sustainable Development Goals (SDGs) are interconnected. The SDGs, set by the United Nations in 2015, aim to create a sustainable society by addressing various aspects such as poverty, climate action, and life on land. Earth Observation comes into play by providing satellite data that can be translated into the targets and variables countries need to report. However, dealing with the massive amounts of data from these satellites is a big data problem, leading to the use of AI and machine learning to derive insights and support decision-makers. Two major players in this operation are governments and the commercial sector, which provide data through various channels. Accessing and using datasets from multiple sources can be a logistical challenge, but companies like Radian are stepping in to help. In summary, Earth Observation, machine learning, and the SDGs are essential tools for creating a sustainable future, and effectively utilizing the vast amounts of data they generate is crucial.
Standardizing metadata for satellite imagery data using STAC:Â STAC standard enables universal access to geospatial datasets, Radian provides AI/ML ready datasets, transforming raw data into ML-ready datasets presents challenges, resources like ML hub offer tutorials and tools to help.
The satellite imagery industry faced challenges in standardizing metadata for optical imagery data, leading to the development of the Spatiotemporal Asset Catalog (STAC) standard. This open-source, community-driven specification enables universal access to geospatial datasets from various providers. Radian, as a neutral agency, contributes to this effort by providing AI and ML ready datasets through their data repository, radianmlhop. Users can search for labeled datasets, such as land cover classes, and access the corresponding imagery for machine learning model development. However, transforming raw satellite imagery into ML-ready datasets presents challenges, including dealing with large volumes of data and time series observations. To help overcome these challenges, resources like ML hub offer tutorials and tools to make the process more manageable. For instance, MIT Sloan Management Review, Boston Consulting Group, and Me, Myself, and AI podcast used raw customer logs to build a better model, demonstrating the potential of leveraging these datasets for AI and ML applications.
Revolutionizing Environmental Monitoring with AI and Satellite Imagery:Â AI and satellite imagery enable governments and organizations to monitor forest cover, prevent deforestation, and address climate change by providing valuable data for environmental monitoring and supporting the achievement of Sustainable Development Goals.
AI and satellite imagery are revolutionizing the way governments and organizations address environmental issues, specifically in the context of monitoring forest cover and preventing deforestation in Africa. The language used by AI chat agents may not be as human-like as we'd prefer, but the data they provide is invaluable. For instance, satellite imagery, which is regularly available and synced with the sun's orbit, can help governments build monitoring systems to detect changes in forested areas and prevent illegal deforestation. This is crucial as forests play a significant role in mitigating climate change by absorbing CO2 emissions. Radiant Earth, an organization that provides access to Earth observation data, can support these efforts by providing governments with the necessary datasets. Furthermore, Radiant Earth also works with communities to gather ground-level data, which can be used to understand yield changes, forest cover changes, and more, contributing to the achievement of various Sustainable Development Goals. Overall, the use of AI and satellite imagery in environmental monitoring is a tangible and impactful solution to addressing climate change and community problems.
Using AI and satellite imagery to empower local communities and governments:Â AI and satellite imagery enable informed decisions in agriculture and disaster response, even when ground data is limited, through machine learning models that classify crop types and synthetic data generated using GANs.
The use of AI and remote sensing technology, specifically satellite imagery, can empower local communities and governments to make informed decisions, particularly in agriculture and disaster response, even when ground data is limited. This is achieved through machine learning models that can classify crop types based on time series satellite imagery and synthetic data generated using GaN's generative adversarial networks. The goal is to enable self-sufficiency in data collection and analysis, with organizations like Ava Labs working in partnership to provide tools and guidance. The application of GANs for data augmentation in the context of data scarcity is an ongoing area of research and development. While the use of simulation for data augmentation and GANs is an emerging trend, Ava Labs have not yet explored this area extensively. For those interested, they can visit isthisplacereal.com to see examples of synthetic satellite imagery and test their ability to distinguish real from synthetic images.
Simulation in Air Science and Geospatial Sector for Climate Modeling:Â Radian is exploring simulation in air science and geospatial sector for climate modeling, using satellite imagery to curate high-quality training data and running competitions to crowdsource ML models for agricultural census problems.
Simulation is a growing field in the air science and geospatial sector, particularly for those integrating AI and ML modeling into climate modeling. Radian hasn't entered this space yet but recognizes its potential for scalability and interpretability. The use of simulation results in machine learning models to project climate changes within different time frames. Radian's approach to solving agricultural census problems involves curating high-quality training data from satellite imagery and running competitions to crowdsource models. The Western Cape Department of Agriculture in South Africa provided the data, and the goal was to automate or semi-automate the process of creating updated crop maps every year. The competition, run on the Zindy platform, exposed the problem to a diverse pool of talents and provided an opportunity for capacity development. The winners' open-source models will be shared soon. For research groups like Joyce's in Africa, these tools can enable new problem-solving opportunities and contribute to capacity development in the AI and geospatial domains.
Collaboration between Radiant Earth and Makaira AI Lab:Â Radiant Earth's resources and focus on capacity building aligns with Makaira AI Lab's mission, leading to valuable collaborations and practical training for students in machine learning and AI using satellite imagery and Earth observation.
Radiant Earth's data and resources are valuable for research institutions like Makaira AI Lab in their efforts to understand and address practical problems, such as deforestation and crop mapping. Additionally, Radiant Earth's focus on capacity building aligns perfectly with Makaira AI Lab's mission to train students in machine learning and AI, particularly in the context of satellite imagery and Earth observation. The collaboration between the two organizations, as demonstrated by their recent machine learning boot camp, is an essential step towards bridging the gap between the growing community of AI practitioners and the remote sensing world. By providing practical training and hands-on experience, Radiant Earth is contributing significantly to the development of a skilled workforce that can effectively address the sustainable development goals.
Applying Geospatial Technology to Agriculture and Food Security:Â Gradient's MLHOP program uses machine learning to improve farmers' economic well-being, food security, and sustainable use of natural resources in developing regions through geospatial technology in agriculture and food security.
The Geospatial sector, which involves Earth observations, can be applied to various areas such as agriculture, food security, land cover, surface water monitoring, drought monitoring, deforestation, and ocean monitoring. The company Gradient, through their MLHOP program, focuses on using machine learning in the agriculture and food security sector due to its significant impact on employment and GDP in developing regions. The ultimate goal is to help governments and stakeholders make informed decisions to improve farmers' economic well-being, food security, and sustainable use of natural resources. The company also aims to train as many people as possible to contribute to the solution, creating a sustainable training ecosystem. The online course, available on the FNG platform, provides access to knowledge and a user community for continuous learning and collaboration.
Empowering Africa with Earth observation data and ML tools:Â Radiant Earth's ML Hub provides accessible model registry, tutorials, and datasets for Earth observation applications, enabling capacity building and efficient solutions for academia, industry, and policy in Africa.
Radiant Earth's ML Hub is making Earth observation data and machine learning tools more accessible to a wider audience in Africa, particularly for those in academia, industry, and policy. The ML Hub offers a model registry, tutorials, and datasets for various applications such as crops, wildfire, and land cover. The API calls are straightforward and simple to use, allowing users to build models or analyze data easily. This initiative addresses the capacity building issue in the growing field of AI for Earth observation by providing both knowledge and data. The ML Hub's open access policy is a step towards empowering end-users, including decision-makers, farmers, and urban developers, to create more efficient and productive solutions for their communities. Radiant Earth looks forward to engaging with more partners and users in the new year and continuing to support the success of their applications.
Explore ML Hub, an open repository for machine learning datasets and contributions:Â ML Hub, an initiative by Radiant Earth Foundation, offers access to a wide range of open datasets for machine learning, welcomes contributions, and provides opportunities for competitions and courses.
ML Hub, an open repository by Radiant Earth Foundation, not only provides access to a wide range of open datasets for machine learning but also welcomes contributions from providers and users. This means that if you have benchmark data you'd like to share with a broader community, you can publish it on ML Hub. Radiant Earth is also working to expand the coverage of the data in terms of geospatial locations and application areas. If you have specific needs or require support, feel free to reach out through the stack workspace or the support channel on their website. ML Hub offers open data sets, competitions, and courses, making it an excellent platform to get involved in the new year. Radiant Earth team members, including Joyce, were present to discuss these opportunities. For more information, visit changelog.com/master or search for changelogmaster in your preferred podcast app. Remember, ML Hub is committed to engaging more users and looks forward to your participation.
